ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI

• URL: http://arxiv.org/abs/2405.06178v1
• Date: Fri, 10 May 2024 01:45:09 GMT
• Title: ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI
• Authors: Yuqi Fang, Junhao Zhang, Linmin Wang, Qianqian Wang, Mingxia Liu,
• Abstract summary: Action is a Python-based and cross-platform toolbox for fMRI analysis. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. It supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data.
• Score: 28.639321546348654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing toolboxes seldom consider fMRI data augmentation, which is quite useful, especially in studies with limited or imbalanced data. Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. In this work, we develop an open-source toolbox, called Augmentation and Computation Toolbox for braIn netwOrk aNalysis (ACTION), offering comprehensive functions to streamline fMRI analysis. The ACTION is a Python-based and cross-platform toolbox with graphical user-friendly interfaces. It enables automatic fMRI augmentation, covering blood-oxygen-level-dependent (BOLD) signal augmentation and brain network augmentation. Many popular methods for brain network construction and network feature extraction are included. In particular, it supports constructing deep learning models, which leverage large-scale auxiliary unlabeled data (3,800+ resting-state fMRI scans) for model pretraining to enhance model performance for downstream tasks. To facilitate multi-site fMRI studies, it is also equipped with several popular federated learning strategies. Furthermore, it enables users to design and test custom algorithms through scripting, greatly improving its utility and extensibility. We demonstrate the effectiveness and user-friendliness of ACTION on real fMRI data and present the experimental results. The software, along with its source code and manual, can be accessed online.

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